Getting started with MovingPandas

MovingPandas provides a trajectory datatype based on GeoPandas. The project home is at https://github.com/anitagraser/movingpandas

The documentation is available at https://movingpandas.readthedocs.io/en/master/

import pandas as pd
import geopandas as gpd
from geopandas import GeoDataFrame, read_file
import shapely as shp
from shapely.geometry import Point, LineString, Polygon
from datetime import datetime, timedelta
import movingpandas as mpd
import hvplot.pandas 

import warnings
warnings.filterwarnings('ignore')

print(f'MovingPandas version {mpd.__version__}')
print(f'GeoPandas version {gpd.__version__}')
print(f'Shapely version {shp.__version__}')

MovingPandas version 0.8.rc1
GeoPandas version 0.10.2
Shapely version 1.7.1

Creating a trajectory from scratch

Trajectory objects consist of a trajectory ID and a GeoPandas GeoDataFrame with a DatetimeIndex. The data frame therefore represents the trajectory data as a Pandas time series with associated point locations (and optional further attributes).

Let's create a small toy trajectory to see how this works:

df = pd.DataFrame([
  {'geometry':Point(0,0), 't':datetime(2018,1,1,12,0,0)},
  {'geometry':Point(6,0), 't':datetime(2018,1,1,12,6,0)},
  {'geometry':Point(6,6), 't':datetime(2018,1,1,12,10,0)},
  {'geometry':Point(9,9), 't':datetime(2018,1,1,12,15,0)}
]).set_index('t')
gdf = GeoDataFrame(df, crs=31256)
toy_traj = mpd.Trajectory(gdf, 1)
toy_traj

Trajectory 1 (2018-01-01 12:00:00 to 2018-01-01 12:15:00) | Size: 4 | Length: 16.2m
Bounds: (0.0, 0.0, 9.0, 9.0)
LINESTRING (0 0, 6 0, 6 6, 9 9)

toy_traj.plot()

<AxesSubplot:>

We can also access the trajectory's GeoDataFrame:

toy_traj.df

	geometry
t
2018-01-01 12:00:00	POINT (0.000 0.000)
2018-01-01 12:06:00	POINT (6.000 0.000)
2018-01-01 12:10:00	POINT (6.000 6.000)
2018-01-01 12:15:00	POINT (9.000 9.000)

toy_traj.df.plot()

<AxesSubplot:>

Loading trajectories from CSV

df = pd.read_csv('../data/geolife_small.csv', delimiter=';')
df

	X	Y	fid	id	sequence	trajectory_id	tracker	t
0	116.391305	39.898573	1	1	1	1	19	2008-12-11 04:42:14+00
1	116.391317	39.898617	2	2	2	1	19	2008-12-11 04:42:16+00
2	116.390928	39.898613	3	3	3	1	19	2008-12-11 04:43:26+00
3	116.390833	39.898635	4	4	4	1	19	2008-12-11 04:43:32+00
4	116.389410	39.898723	5	5	5	1	19	2008-12-11 04:43:47+00
...	...	...	...	...	...	...	...	...
5903	116.337194	39.926232	6993	6993	867	5	2	2009-02-25 14:31:04+00
5904	116.337207	39.926237	6994	6994	868	5	2	2009-02-25 14:31:09+00
5905	116.337259	39.926207	6995	6995	869	5	2	2009-02-25 14:31:14+00
5906	116.337290	39.926201	6996	6996	870	5	2	2009-02-25 14:31:19+00
5907	116.337332	39.926186	6997	6997	871	5	2	2009-02-25 14:31:24+00

5908 rows × 8 columns

traj_collection = mpd.TrajectoryCollection(df, 'trajectory_id', t='t', x='X', y='Y')
print(traj_collection)

TrajectoryCollection with 5 trajectories

traj_collection.plot(column='trajectory_id', legend=True, figsize=(9,5))

<AxesSubplot:>

Loading trajectories from a GeoPackage

gdf = read_file('../data/geolife_small.gpkg')
gdf

	id	sequence	trajectory_id	tracker	t	geometry
0	1	1	1	19	2008-12-11 04:42:14+00	POINT (116.39131 39.89857)
1	2	2	1	19	2008-12-11 04:42:16+00	POINT (116.39132 39.89862)
2	3	3	1	19	2008-12-11 04:43:26+00	POINT (116.39093 39.89861)
3	4	4	1	19	2008-12-11 04:43:32+00	POINT (116.39083 39.89863)
4	5	5	1	19	2008-12-11 04:43:47+00	POINT (116.38941 39.89872)
...	...	...	...	...	...	...
5903	6993	867	5	2	2009-02-25 14:31:04+00	POINT (116.33719 39.92623)
5904	6994	868	5	2	2009-02-25 14:31:09+00	POINT (116.33721 39.92624)
5905	6995	869	5	2	2009-02-25 14:31:14+00	POINT (116.33726 39.92621)
5906	6996	870	5	2	2009-02-25 14:31:19+00	POINT (116.33729 39.92620)
5907	6997	871	5	2	2009-02-25 14:31:24+00	POINT (116.33733 39.92619)

5908 rows × 6 columns

After reading the trajectory point data from file, we want to construct the trajectories.

traj_collection = mpd.TrajectoryCollection(gdf, 'trajectory_id', t='t')
print(traj_collection)

TrajectoryCollection with 5 trajectories

traj_collection.plot(column='trajectory_id', legend=True, figsize=(9,5))

<AxesSubplot:>

traj_collection.plot()

<AxesSubplot:>

Accessing individual trajectories

my_traj = traj_collection.trajectories[1]
print(my_traj)

Trajectory 2 (2009-06-29 07:02:25 to 2009-06-29 11:13:12) | Size: 897 | Length: 38764.6m
Bounds: (116.319212, 39.971703, 116.592616, 40.082514)
LINESTRING (116.590957 40.071961, 116.590905 40.072007, 116.590879 40.072027, 116.590915 40.072004, 

my_traj.plot(linewidth=5, capstyle='round', figsize=(9,3))

<AxesSubplot:>

To visualize trajectories in their geographical context, we can also create interactive plots with basemaps:

my_traj.hvplot(width=500, height=300, line_width=7.0, tiles='OSM')